A process control or automation system is used to automatically control an industrial process such as chemical, oil refineries, paper and pulp mill, and power plants. The process automation system often uses a network to interconnect various system components, such as sensors, actuators, controllers, and operator terminals. Process automation involves using computer technology and software engineering to help power plants and factories operate more efficiently and safely.
Process simulation is a model-based representation of industrial processes and unit operations in software for studying and analyzing the behavior and performance of actual or theoretical systems. Simulation studies are performed, not on the real-world system, but on a (usually computer-based) model of the system created for the purpose of studying certain system dynamics and characteristics. The purpose of any model is to enable its users to draw conclusions about the real system by studying and analyzing the model response. The major reasons for developing a model, as opposed to analyzing the real system, include economics, unavailability of a “real” system, and the goal of achieving a deeper understanding of the relationships between the elements of the system.
Process simulation always uses models which introduce approximations and assumptions but allow the description of a property over a wide range of properties, such as temperatures and pressures, which might not be covered by real data. Models also allow interpolation and extrapolation—within certain limits—and enable the search for conditions outside the range of known properties. In process automation, the simulator may use measurements to show not only how the plant is working but to simulate different operating modes and find the optimal strategy for the plant.
Simulation can be used in task or situational training areas in order to allow operators to anticipate certain situations and be able to react properly as well as to test and select alternatives based on some criteria, to test why certain phenomena occur in the operations of the system under consideration, to gain insight about which variables are most important to performance and how these variables interact, to identify bottlenecks in the process, to better understand how the system really operates (as opposed to how everyone thinks it operates), and to compare alternatives and reduce the risks of decisions.
A basic process simulator is run with no real-time connection to a simulated process. A tracking simulator, on the other hand, has the ability to adapt its behavior to reality. A tracking simulator is a process simulator that runs in real-time in parallel with the real process and is provided with a connection to the real process. More specifically, the tracking simulator receives process measurements from the real process and is able to correct its own behavior (models) by comparing the real process measurements to the simulator outputs. PCT/FI2010/050564 discloses an example of such tracking simulator.
US2008/0027704 discloses a real-time synchronized control and simulation within a process plant. A simulator is run in parallel with the actual control system and the actual process. The operator can toggle the simulator between two alternative modes of operation: a tracking mode and a prediction mode. In a tracking mode the simulator can correct or update its models based on the data from the real process. In the prediction mode the simulator performs simulation of the process control system over some future time horizon, to perform “what-if” scenarios, for example. In the prediction mode the simulator corresponds to a basic process simulator run with no real-time connection to a simulated process.
Disturbances enter or affect the process and tend to drive the controlled dependent variables away from their desired value or setpoint condition. Typical disturbances include changes in demand for product, or in the supply of feed material. The control system must adjust the dependent variable so the setpoint value of the independent variable is maintained despite the disturbances. If the setpoint is changed, the independent variable must be changed to adjust the controlled variable to its new desired value.
Despite their differences, continuous-process industries share underlying characteristics: they maintain continuous operations in facilities that represent substantial start-up costs and time, but can be interrupted or disrupted by minor disturbances. If the product stream is disrupted or the process is not run optimally, lost productivity and lost product can create a large financial burden. Therefore it is important for the operator to monitor and supervise the process in real-time. Control rooms are central facilities used to manage large systems such as industrial processes. As the operators of large systems are asked to perform more efficiently, use more sophisticated control systems, and take on more duties and responsibilities, developers of control room equipment have sought to improve operators' ability to interact effectively with their systems.
Modern process plants generate a vast amount of real-time data that often has to be stored and processed efficiently for later analysis. All measurements, setpoints, controller outputs, device statuses, motor starts, alarms, operation tracking etc. may be stored into so called historian data-bases. A historian database may be integrated with other parts of a control system, such as controls, engineering and control room functionalities. By means of the history data collected from the process plant over time, trend charts can be created that show process trends in data over time. In modern control rooms, long history trends are usually available for the users. As all processes vary, single point measurements can be misleading. Displaying historical data over time increases understanding of the real performance of a process.